207 citations · 432 across the 37 of their papers we have counts for
8 papers · 1 filter
Walking the Score Manifold: Continuous-time Generative Dynamics on Learned Data Manifolds
Jan Tauberschmidt, Brian B. Moser, Stanislav Frolov +3
Generative modeling of time-dependent data is typically formulated on a discrete temporal grid, restricting supervision to the observed timestamps in the training data. We instead…
Layers Matter: Why Continual Learning Regularization Should Be Layer-Adaptive
Brian B. Moser, Ahmed Anwar, Tobias Christian Nauen +5
Continual learning regularizers like EWC fight forgetting by penalizing changes from previous-task parameters with per-parameter importance, typically diagonal Fisher values. Per-p…
Hyperspherical Forward-Forward with Prototypical Representations
Shalini Sarode, Brian Moser, Joachim Folz +4
The Forward-Forward (FF) algorithm presents a compelling, bio-inspired alternative to backpropagation. However, while efficient in training, it has a computationally prohibitive in…
PRISM: Diversifying Dataset Distillation by Decoupling Architectural Priors
Brian B. Moser, Shalini Sarode, Federico Raue +6
Dataset distillation (DD) promises compact yet faithful synthetic data, but existing approaches often inherit the inductive bias of a single teacher model. As dataset size increase…
SubZeroCore: A Submodular Approach with Zero Training for Coreset Selection
Brian B. Moser, Tobias C. Nauen, Arundhati S. Shanbhag +4
The goal of coreset selection is to identify representative subsets of datasets for efficient model training. Yet, existing approaches paradoxically require expensive training-base…
HyperCore: Coreset Selection under Noise via Hypersphere Models
Brian B. Moser, Arundhati S. Shanbhag, Tobias C. Nauen +4
The goal of coreset selection methods is to identify representative subsets of datasets for efficient model training. Yet, existing methods often ignore the possibility of annotati…
Unifying VXAI: A Systematic Review and Framework for the Evaluation of Explainable AI
David Dembinsky, Adriano Lucieri, Stanislav Frolov +3
Modern AI systems frequently rely on opaque black-box models, most notably Deep Neural Networks, whose performance stems from complex architectures with millions of learned paramet…
A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advances
Brian B. Moser, Arundhati S. Shanbhag, Stanislav Frolov +3
Coreset selection targets the challenge of finding a small, representative subset of a large dataset that preserves essential patterns for effective machine learning. Although seve…